Expanding global data coverage of climate change support

Jordi Mas

Universitat Oberta de Catalunya

Marc Guinjoan

Universitat Autònoma de Barcelona

Introduction

Introduction

Despite there is a wide interest in climate change mitigation (Andre et al. 2024; Falkner 2016; Mayer, Shames, and Wronski 2017), global coverage surveys only began in recent years; e.g. WRP (2019), PCV (2021).

  • Generally, survey data about climate change is:
    • Skewed: Few low-income countries, small islands.
    • Inconsistent: Variations in wording, response scales, and survey structure.
    • Limited: Longitudinal data are rare (Eurobarometer).

Introduction

We aim to harmonize disparate survey data on climate attitudes into a single, comparable latent measure. With this is mind, we will generate three complementary datasets:

  1. Cross-sectional: Maximize country coverage.
  2. Country-year data: Maximize matrix consistency.
  3. Longitudinal: Maximize temporal depth.

Data and methodology

Data collection

  • Questions about climate change attitudes from 12 major global/regional surveys:
    • G: World Risk Poll, WVS, ISSP, etc.
    • R: Eurobarometer, Afrobarometer, CAB, etc.
  • 8,000+ item-country-year observations (2010-2024)
    • Over 3.9 million of individual responses.
    • 173 countries

Data collection

Figure 1: Global coverage of climate change attitude survey data (2010–2024)

Data collection

Figure 2: Country-year climate change attitudes data coverage (2010–2024)

Exploratory analysis

Items can represent different dimensions of CC support.

  • PCA between high coverage items reveals unidimensional structure of the data.

Figure 3: Parallel analysis scree plot for PCA (27 items; N > 40)

Exploratory analysis

Item selection criteria:

  • Retained medium- to high-coverage items with a correlation ≥ 0.3 to the principal component.
    • Ensures conceptual coherence with the latent dimension of climate support
  • Included low-coverage items (fewer than 15 countries) regardless of correlation to preserve regional diversity.
    • Central Asia Barometer, Arab Barometer, etc.

Statistical model

Bayesian ordinal Item Response Theory (IRT) with partial pooling (e.g. Claassen 2019).

  • Handles ordinal data.
  • Useful with missing data and uneven coverage.
  • Temporal smoothing to track trends from 2010–2024

Three outputs of the dataset

Cross-sectional dataset (2019-24)

167 countries; world GDP (99.5%); population (98.6%).

Reasonably balanced: 60%-60% dem, 24k-18k GDPcap.

Cross-sectional dataset (2019-24)

Country-year panel (2010–2024)

Nearly 72 countries (57% global GDP, 28% pop).

Unbalanced: 70%-40% democracies, 31k-13k GDPc

Extended time series (1990s–2024)

Nearly 50 countries.

Unbalanced: Primarily OECD, democracies and high GDPc.

Conclusion (I)

This research presents a harmonized and globally representative dataset of public attitudes toward climate change.

  • Addresses major limitations in existing data:
    • Skewed coverage (overrepresenting high-income democracies).
    • Inconsistent survey formats and item wording.
    • Sparse and fragmented longitudinal data.

Conclusion (II)

  • Applies a Bayesian ordinal IRT model to estimate a latent measure of climate support across countries and years.

  • Outputs three complementary datasets.

    • Cross-sectional: over 165 countries.
    • Country-year panel: consistent data 2010-2024.
    • Longitudinal series: historical trends for ~50 countries, mainly in the OECD

References

Andre, Peter, Teodora Boneva, Felix Chopra, and Armin Falk. 2024. “Globally Representative Evidence on the Actual and Perceived Support for Climate Action.” Nature Climate Change 14 (3): 253–59. https://doi.org/10.1038/s41558-024-01925-3.
Claassen, Christopher. 2019. “In the Mood for Democracy? Democratic Support as Thermostatic Opinion.” American Political Science Review 113 (3): 668–83. https://doi.org/10.1017/S000305541900005X.
Falkner, Robert. 2016. “The Paris Agreement and the New Logic of International Climate Politics.” International Affairs 92 (5): 1107–25.
Mayer, Andreas, Shauna L. Shames, and Laura Wronski. 2017. “Predicting Support for Climate Change Mitigation Policies: The Role of Values, Beliefs, and Perceived Risk.” Political Behavior 39 (4): 869–89. https://doi.org/10.1007/s11109-016-9389-y.